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    <id>5008</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
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    <edition/>
    <issue/>
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    <type>conferencepresentation</type>
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    <completedDate>2022-04-26</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">AI to quantify biological diversity</title>
    <abstract language="eng">The primary consumers of plant exudates – in exact fungi and bacteria, are representative for the soil succession level from bare soil, which is bacterial dominant to old growth forest constitute by fungal dominance. In a specified level of soil succession, a special kind of plant family benefits on the Fungal to Bacteria Ratio. The ability to determine this ratio in situ without complex chemical applications is part of the project Electronical Laboratory for Intelligent Soil Examination (ELISE). Several mechanical and optical tests on soil samples are covered within this Project. To analyze the fungal to bacteria ratio, samples are prepared automatically – in a defined and reproductive procedure – to generate slides for shadowing microscopy. The samples are observed by a camera, which is attached to a transmitted light microscope. The automatic analysis, done with computer vision algorithms, aims to quantify bacterial and fungal biomass in the actual sample view. Moreover, the algorithm can classify organisms according to their color and shape. &#13;
To get a processable picture, several images from different focal levels must be taken through the sample thickness. Parts of each image, are in focus at the actual layer, are merged to a whole depth of field picture, by focus stacking. &#13;
This produced picture is used to classify, locate and quantify – in first step filamentous organisms e.g. fungal by image sematic segmentation. The result represents an image sized mask, which indicates the class of fungi with class equivalate values at the pixel positions – covered by the organism. This information is used to calculate the fungal mass per gram soil.&#13;
To quantify the bacterial biomass two approaches are implemented. For low density of bacterial existence, the individual bacteria is counted for a part of the field of view by an image detection algorithm to be extrapolate afterwards to the mass per gram soil.  For high density of bacterial occurrence, specified regions of interest with only bacteria present are chosen. An image classification which has been pretrained by pictures of bacterial density patterns – previously determent by making the sample countable due to preforming sample dilutions, is done. The second option for high density bacterial count is, to automatically preform dilutions until the image detection is confidently countable.&#13;
To ensure a usable confidence score a statistical approach of many fields of view is taken.</abstract>
    <parentTitle language="deu">LANDSUPPORT Final Conference and the Save Our Soils Workshop, 27 -28 APRIL 2022, Portici, Italy</parentTitle>
    <identifier type="url">https://www.landsupport.eu/wp-content/uploads/2022/04/210422-abstract-poster-v2.pdf</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-50080</identifier>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Tobias Heinrich</author>
    <author>Hermann Ketterl</author>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othforschungsschwerpunkt" number="16317">Sensorik</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/5008/poster.pdf</file>
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